胶质瘤已成为人类最致命的脑瘤。及时诊断这些肿瘤是有效癌症治疗的一项重要步骤。磁共振成像（MRI）通常提供了对脑病变的无创检查。然而，从MRI扫描中手动检查肿瘤需要大量时间，并且是容易出错的过程。因此，肿瘤的自动诊断在胶质瘤的临床管理和手术干预中发挥着至关重要的作用。在这项研究中，我们提出了一种基于卷积神经网络（CNN）的框架，用于从3D MRI扫描中非侵入性地分级肿瘤。所提出的框架包括两种新型CNN体系结构。第一个CNN体系结构执行多模MRI体积中肿瘤的分割。所提出的分割网络利用空间和通道注意力模块在层间重新校准特征图。第二个网络利用多任务学习策略，根据三种胶质瘤分级任务执行分类，包括将肿瘤表征为低级或高级，识别1p19q和异柠檬酸脱氢酶（IDH）状态。我们进行了多次实验来评估我们方法的性能。广泛的实验观察表明，所提出的框架比多种最先进的方法实现了更好的性能。我们还执行了Welch's-t检验，以显示分级结果的统计显著性。本研究的源代码可在https://github.com/prasunc/Gliomanet上找到。

Glioma has emerged as the deadliest form of brain tumor for human beings. Timely diagnosis of these tumors is a major step towards effective oncological treatment. Magnetic Resonance Imaging (MRI) typically offers a non-invasive inspection of brain lesions. However, manual inspection of tumors from MRI scans requires a large amount of time and it is also an error-prone process. Therefore, automated diagnosis of tumors plays a crucial role in clinical management and surgical interventions of gliomas. In this study, we propose a Convolutional Neural Network (CNN)-based framework for non-invasive grading of tumors from 3D MRI scans. The proposed framework incorporates two novel CNN architectures. The first CNN architecture performs the segmentation of tumors from multimodel MRI volumes. The proposed segmentation network leverages the spatial and channel attention modules to recalibrate the feature maps across the layers. The second network utilizes the multi-task learning strategy to perform the classification based on the three glioma grading tasks which include characterization of tumor into low-grade or high-grade, identification of 1p19q, and Isocitrate Dehydrogenase (IDH) status. We have carried out several experiments to evaluate the performance of our method. Extensive experimental observations indicate that the proposed framework achieves better performance than several state-of-the-art methods. We have also executed Welch's- t test to show the statistical significance of grading results. The source code of this study is available at https://github.com/prasunc/Gliomanet.